ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Feature Selection

  1. J. Jordon, J. Yoon, M. van der Schaar, "KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks," International Conference on Learning Representations (ICLR), 2019. [Link] - Selected as oral presentation
  2. J. Yoon, J. Jordon, M. van der Schaar, "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019. [Link]
  3. C. Rietschel, J. Yoon, and M. van der Schaar, "Feature Selection for Survival Analysis with Competing Risks using Deep Learning," NIPS Machine Learning for Health Workshop 2018. [Link]
  4. O. Atan, W. R. Zame, M. van der Schaar, "Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features," Machine Learning, 2018. [Link]
  5. A. M. Alaa, M. van der Schaar, "Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning," Scientific Reports, 2018. [Link]
  6. E. Soltanmohammadi, M. Naraghi-Pour, and M. van der Schaar, " Context-based Unsupervised Ensemble Learning and Feature Ranking," Machine Learning, pp. 1-27, June 2016. [Link]
  7. J. Yoon, C. Davtyan, M. van der Schaar, "Discovery and Clinical Decision Support for Personalized Healthcare," IEEE J. Biomedical and Health Informatics, 2016. [Link]
  8. O. Atan, C. Tekin, J. Xu and M. van der Schaar, "Discovering Action-Dependent Relevance: Learning from Logged Data," Submitted, 2015. [Link]
  9. O. Atan and M. van der Schaar, "Discover Relevant Sources : A Multi-Armed Bandit Approach," Submitted, 2015. [Link]
  10. C. Tekin and M. van der Schaar, "Discovering, Learning and Exploiting Relevance," Neural Information Processing Systems (NIPS), 2014. [Link]